Information processing device and information processing method for specifying target point of an object

ABSTRACT

According to an embodiment, an information processing device includes an acquirer, a categorizer, a shape fitter, and a specifier. The acquirer is configured to acquire position information of each of a plurality of detection points. The categorizer is configured to cluster the detection points into one or more detection point groups each representing an object. The shape fitter is configured to apply a predetermined shape model to each detection point group on the basis of the position information of each of the clustered detection points included in the each detection point group. The specifier is configured to specify a target point of the object on the basis of the shape model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2016-107455, filed on May 30, 2016; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an informationprocessing device and an information processing method.

BACKGROUND

Technologies of specifying target points of an object to calculate theposition, the moving speed, and the like of the object have beendisclosed.

For example, Japanese Laid-open Patent Publication No. 2010-249690discloses that when the center of an object is selected as the positionof the object when the object is completely inside a detection area.Japanese Laid-open Patent Publication No. 2010-249690 also disclosesthat a front-end part or a back-end part of the object in the movingdirection of the object is selected when the object is only partiallyinside the detection area. According to Japanese Laid-open PatentPublication No. 2010-249690, selected detection points are used tocalculate the moving speed. Japanese Patent No. 4140118 discloses that aposition of a data closest to a travel path, among a group of datarepresenting an identical object, is set as a target point of theobject.

However, a target point of an object has been conventionally calculatedby simply selecting a detection point on the object, and it has beendifficult to accurately specify the target point of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an information processing device;

FIG. 2 is a block diagram illustrating the configuration of theinformation processing device;

FIG. 3 is a pattern diagram illustrating a data configuration of shapemodel management information;

FIGS. 4A to 4D are explanatory diagrams of processing executed by aprocessing circuit;

FIG. 5 is a flowchart of the processing executed by the processingcircuit;

FIGS. 6A to 6C are explanatory diagrams illustrating specification of atarget point;

FIG. 7 is an explanatory diagram of specification of different positionsas target points;

FIG. 8 is a block diagram illustrating the configuration of aninformation processing device;

FIGS. 9A and 9B are explanatory diagrams of processing executed by aprocessing circuit;

FIG. 10 is a flowchart of the processing executed by the processingcircuit;

FIG. 11 is a block diagram illustrating the configuration of aninformation processing device;

FIG. 12 illustrates explanatory diagrams of specification of a targetpoint;

FIG. 13 illustrates explanatory diagrams of specification of a targetpoint;

FIG. 14 is a flowchart of processing executed by a processing circuit;and

FIG. 15 is a hardware configuration diagram.

DETAILED DESCRIPTION

According to an embodiment, an information processing device comprisinga memory; and processing circuitry configured to operate as: an acquirerconfigured to acquire position information of detection points; acategorizer configured to cluster the detection points into one or moredetection point groups each representing an object; a shape fitterconfigured to fit a predetermined shape model into each detection pointgroup on the basis of the position information of the clustereddetection points belonging to the each detection point group; and aspecifier configured to specify a target point of the object on thebasis of the shape model.

An information processing device and an information processing methodwill be described in detail below with reference to the accompanyingdrawings.

First Embodiment

FIG. 1 is a diagram illustrating an exemplary information processingdevice 10 according to the present embodiment. The informationprocessing device 10 specifies a target point T of an object 30.

The present embodiment exemplarily describes a case in which theinformation processing device 10 is mounted on a vehicle 12. However,the information processing device 10 is not limited to the configurationin which the information processing device 10 is mounted on the vehicle12. The information processing device 10 may be mounted on a stationaryobject such as an external device fixed to the ground, for example, aroad. Alternatively, the information processing device 10 may be mountedon a moving object other than the vehicle 12.

The object 30 is an object, the target point T of which is specified bythe information processing device 10.

Specifically, the object 30 is an object existing outside of a sensor10G (to be described in detail later) and observed by the sensor 10G.

The object 30 may be any of a moving object and a stationary object. Themoving object is a movable object. The moving object is, for example, avehicle (a motorcycle, an automatic four-wheel vehicle, or a bicycle), atrolley, an object that can fly (a manned airplane, or an unmannedairplane (for example, a drone)), a robot, or a person. The stationaryobject is an object fixed to the ground. The stationary object is animmobile object or an object at rest relative to the ground. Thestationary object is, for example, a guardrail, a pole, a parkedvehicle, or a traffic sign. The object 30 may be any of a living objectand a non-living object. The living object is, for example, a person, ananimal, or a plant. The non-living object is, for example, a vehicle, anobject that can fly, or a robot.

The target point T of the object 30 is a single point representing theobject 30. The target point T of the object 30 is used, for example, toderive the position and the speed of the object 30. The target point Tof the object 30 may be used as the position of the object 30. In thepresent embodiment, the information processing device 10 specifies thetarget point T of the object 30. A method of specifying the target pointT will be described later in detail.

The information processing device 10 includes a processing circuit 10A,an output circuit 10C, and the sensor 10G.

The sensor 10G acquires observation information on the outside. Theobservation information represents a result of observing the surroundingof an installation position of the sensor 10G. In the presentembodiment, the observation information is information from whichposition information on detection points around the sensor 10G can bederived.

The position information on a detection point is informationrepresenting the distance from the sensor 10G to the detection point andthe direction of the detection point with respect to the sensor 10G. Thedistance and the direction can be expressed with, for example, positioncoordinates indicating a relative position of the detection point withrespect to the sensor 10G, position coordinates indicating the absoluteposition of the detection point, and a vector.

Each detection point represents a point individually observed by thesensor 10G outside of the sensor 10G. For example, the sensor 10G emitslight to the surrounding of the sensor 10G and receives light reflectedat a reflection point. This reflection point corresponds to thedetection point. A plurality of reflection points may be used as onedetection point.

In this manner, the sensor 10G obtains observation information includingthe emission direction of light to each detection point (direction ofthe detection point with respect to the sensor 10G) and informationrelated to light reflected at each detection point. The informationrelated to the reflected light includes, for example, an elapsed timefrom emission of light to reception of the reflected light, and theintensity of the received light (or the attenuation ratio of theintensity of the received light relative to the intensity of the lightemitted). The processing circuit 10A to be described later derives thedistance from the sensor 10G to the detection point by using thiselapsed time and the like.

The sensor 10G is, for example, an image capturing device or a distancesensor (a millimeter-wave radar or a laser sensor). The image capturingdevice obtains captured image data (hereinafter referred to as acaptured image) through image capturing. The captured image data is, forexample, digital image data including a pixel value defined for eachpixel, or a depth map defining the distance from the sensor 10G to eachpixel. The laser sensor is, for example, a two-dimensional laser imagingdetection and ranging (LIDAR) sensor installed in parallel to ahorizontal plane, or a three-dimensional LIDAR sensor.

A LIDAR sensor emits pulsed laser light to the surrounding and receivesthe laser light reflected at a detection point of the object 30. Then,the LIDAR sensor obtains observation information including the emissiondirection of the laser light and an elapsed time from the emission ofthe laser light to the reception thereof. Then, the processing circuit10A to be described later calculates the distance between the sensor 10Gand the detection point in the emission direction based on the elapsedtime included in the observation information of the detection point.Accordingly, the processing circuit 10A to be described later acquiresthe position information of each detection point (to be described indetail later). The LIDAR sensor (the sensor 10G) may calculate thedistance between the sensor 10G and the detection point in the emissiondirection based on the emission direction of the laser light and theelapsed time from the emission of the laser light to the receptionthereof.

The present embodiment exemplarily describes a case in which the sensor10G is a two-dimensional LIDAR sensor.

The sensor 10G may acquire observation information on each planeincluded in a surface of the object 30. Alternatively, the sensor 10Gmay acquire captured images in a plurality of image capturing directionsdifferent from each other. The information processing device 10 mayinclude a plurality of sensors 10G. In this case, the informationprocessing device 10 may include the sensors 10G of the same kind orinclude a plurality of sensors 10G, at least one of which is of adifferent kind. When the information processing device 10 includes aplurality of sensors 10G, the processing circuit 10A to be describedlater may use, as observation information, integration of observationinformation obtained by these sensors 10G.

The output circuit 10C outputs various kinds of information. In thepresent embodiment, the output circuit 10C outputs output information.The output information includes information related to the target pointT of the object 30. Specifically, the output information includes atleast one of information representing the position of the target point Tof the object 30, and a moving direction, a moved distance, and a movingspeed of the object 30 derived based on the target point T. Theinformation representing the position of the target point T of theobject 30 may be a position relative to the sensor 10G or an absoluteposition.

The output circuit 10C has, for example, a communication function oftransmitting the output information, a display function of displayingthe output information, and a sound outputting function of outputtingsound representing the output information. For example, the outputcircuit 10C includes a communication circuit 10D, a display 10E, and aspeaker 10F.

The communication circuit 10D transmits the output information toanother device. For example, the communication circuit 10D transmits theoutput information through the well-known communication line. Thedisplay 10E displays the output information. The display 10E is, forexample, the well-known liquid crystal display (LCD), a projectiondevice, or a light. The speaker 10F outputs sound representing theoutput information.

The following describes the configuration of the information processingdevice 10 in detail. FIG. 2 is a block diagram of an exemplaryconfiguration of the information processing device 10.

The information processing device 10 specifies the target point T of theobject 30. The information processing device 10 is, for example, adedicated or generalized computer. The information processing device 10includes the processing circuit 10A, a storage circuit 10B, the outputcircuit 10C, the sensor 10G, and an input device 10H. As describedabove, the output circuit 10C includes the communication circuit 10D,the display 10E, and the speaker 10F.

The processing circuit 10A, the storage circuit 10B, the output circuit10C, the sensor 10G, and the input device 10H are connected with eachother through a bus 10I. The processing circuit 10A may be connected ina wired or wireless manner with the storage circuit 10B, the outputcircuit 10C (the communication circuit 10D, the display 10E, and thespeaker 10F), the sensor 10G, and the input device 10H. The processingcircuit 10A may be connected through a network with at least one of thestorage circuit 10B, the output circuit 10C (the communication circuit10D, the display 10E, and the speaker 10F), the sensor 10G, and theinput device 10H.

The input device 10H receives inputting of various kinds of instructionsand information from a user. The input device 10H is, for example, apointing device such as a mouse or a track ball, or an input device suchas a keyboard.

The storage circuit 10B stores therein various kinds of data. Thestorage circuit 10B is, for example, a semiconductor memory element suchas a random access memory (RAM) or a flash memory, a hard disk, or anoptical disk. Alternatively, the storage circuit 10B may be a storagedevice provided outside of the information processing device 10.Alternatively, the storage circuit 10B may be a storage medium.Specifically, the storage medium may store or temporarily store acomputer program and various kinds of information downloaded through,for example, a local area network (LAN) and the Internet. The storagecircuit 10B may include a plurality of storage media.

In the present embodiment, the storage circuit 10B stores therein shapemodel management information 10J. The shape model management information10J is information for managing a shape model. The shape modelmanagement information 10J is not limited to a particular data format.For example, the shape model management information 10J is a database ora table.

The shape model is a model representing the shape of the surface of theobject 30. Specifically, the shape model represents a shape indicated byan outline of the object 30 when viewed from outside. In other words,the shape model represents the surface shape of the object 30, which isobservable by, for example, a sensor.

The shape model may be any model representing the surface shape of theobject 30, and the format thereof is not limited. For example, when theobject 30 is an automatic four-wheel vehicle, a rectangle approximatingthe surface shape of the automatic four-wheel vehicle projected onto thehorizontal plane is used as the shape model of the object 30.Alternatively, the shape model may be, for example, a plane shape suchas a polygon or an ellipse, a combination of one line segment or more,or a cubic shape (for example, rectangular parallelepiped).

FIG. 3 is a pattern diagram illustrating an exemplary data configurationof the shape model management information 10J. The shape modelmanagement information 10J includes a shape model, an object type, apossible size range, and a candidate point in association with eachother.

A shape model registered to the shape model management information 10Jmay be characteristic information indicating characteristics of theshape model, or information (a square or a rectangle, for example)indicating a shape represented by the shape model.

The object kind is information indicating the kind of the object 30having the shape represented by the shape model. The possible size rangeindicates a possible range of the size of the object 30 of thecorresponding kind in observation information. The possible size rangeis expressed with, for example, the range of the height h of the object30 of the corresponding kind in a captured image as observationinformation (for example, a length of the object 30 in a first directionin a two-dimensional plane along the captured image or the horizontalplane), and the range of the width w (for example, a length in a seconddirection orthogonal to the first direction in the two-dimensionalplane) of the object 30. The format of the expression of the possiblesize range is not limited to the format illustrated in FIG. 3.

The candidate point is a point in the shape model, which is a candidatefor the target point T. In the shape model management information 10J,at least two candidate points are set to one shape model in advance.

The candidate point is a point in the shape model, indicating the object30 of the corresponding kind.

For example, the candidate point is a point in the object 30 having thecorresponding shape model, which is likely to be externally observed.When a point is likely to be externally observed, the probability orfrequency of being observed when the object 30 is observed in variousdirections is equal to or higher than a threshold. Specifically, thecandidate point is preferably a point observable by the sensor 10G at afrequency equal to or higher than the threshold when the object 30 andthe sensor 10G are in different positional relations. This threshold isset in advance. The threshold may be changeable as appropriate inaccordance with an operation instruction input through the input device10H by the user.

Specifically, when the shape model is a polygon such as a square or arectangle, the candidate point is each apex (for example, four apexesfor a rectangle shape) as an intersection point of two adjacent sides ofthe polygon. Alternatively, when the shape model is a polygon, thecandidate point may be the middle point of each side of the polygon oran intersection point of line segments.

The candidate point may be a point indicating a characteristic shape ormaterial of the object 30 having the corresponding shape model. Forexample, when the sensor 10G is a LIDAR sensor, the candidate point ofthe shape model is set to be a region in which laser light emitted fromthe LIDAR sensor is correctly reflected. When the sensor 10G is a LIDARsensor, the candidate point of the shape model is preferably not set tobe a region (a black region or a transparent region) in which laserlight emitted from the sensor 10G is not correctly reflected.

When the object 30 is a mixture of a rigid body and a non-rigid body,the candidate point may be set to be the region of the rigid body. Forexample, when the object 30 is a bicycle on which a person is riding, apart corresponding to the person potentially deforms. For this reason,in this case, the candidate point can be set to be the body (forexample, a frame part) of the bicycle.

The candidate point may include a point inside the object 30 having thecorresponding shape model. The inside point is a point inside theoutline of the object 30, including an externally invisible point. Forexample, the candidate point may include a point indicating thebarycenter of the object 30 having the shape model.

The shape model management information 10J may include at leastassociation of a shape model and a plurality of candidate points. Thus,the shape model management information 10J may include no registrationof at least one of the object type and the possible size range. Theshape model management information 10J may be set in advance. The shapemodel management information 10J may be changeable as appropriate inaccordance with, for example, an operation instruction through the inputdevice 10H by the user.

The shape model management information 10J may be association of a shapemodel and a plurality of candidate points corresponding to each ofvarious directions in which one object 30 is viewed.

The description continues with reference to FIG. 2. The processingcircuit 10A will be described below. The processing circuit 10A has anacquisition function 10K, a categorization function 10L, a shape fittingfunction 10M, a specification function 10N, and an output controlfunction 10P.

Each processing function of the processing circuit 10A is stored in thestorage circuit 10B in the form of a computer-executable program. Theprocessing circuit 10A is a processor configured to achieve the functioncorresponding to each program by reading the storage circuit 10B fromthe program and executing the program.

Having read each program, the processing circuit 10A has thecorresponding function illustrated in the processing circuit 10Aillustrated in FIG. 2. In the following description with reference toFIG. 2, the acquisition function 10K, the categorization function 10L,the shape fitting function 10M, the specification function 10N, and theoutput control function 10P are achieved by the single processingcircuit 10A.

The processing circuit 10A may be configured as a combination of aplurality of independent processors for achieving the respectivefunctions. In this case, each processor achieves a function by executinga computer program. Each processing function is achieved as a computerprogram, and these computer programs may be executed by one processingcircuit, or a particular function may be achieved by an independentlydedicated program execution circuit.

The “processor” used in the present embodiment and embodiments to bedescribed later is, for example, a circuit of a central processing unit(CPU), a graphical processing unit (GPU), an application specificintegrated circuit (ASIC), or a programmable logic device (for example,a simple programmable logic device (SPLD), a complex programmable logicdevice (CPLD), or a field programmable gate array (FPGA)). The processorachieves a function by reading and executing a computer program storedin the storage circuit 10B. The computer program may be directlyincorporated in a circuit of the processor instead of being stored inthe storage circuit 10B. In this case, the processor achieves a functionby reading and executing the computer program incorporated in thecircuit.

The acquisition function 10K is an exemplary acquirer. The acquisitionfunction 10K acquires position information of detection points.

The acquisition function 10K acquires observation information on thesurrounding of the sensor 10G from the sensor 10G. As described above,the observation information is observation information on the outside ofthe sensor 10G, from which position information of each of detectionpoints around the sensor 10G can be derived. The acquisition function10K derives the position information of each detection point from theobservation information. In this manner, the acquisition function 10Kacquires the position information of the detection points.

Specifically, assume that the sensor 10G is a LIDAR sensor. In thiscase, the acquisition function 10K acquires, from the sensor 10G,observation information including the emission direction of laser lightand the elapsed time from the emission of the laser light to thereception thereof. Then, the acquisition function 10K calculates, fromthe elapsed time, the distance between the sensor 10G and each detectionpoint in the emission direction. In this manner, the acquisitionfunction 10K acquires, for each detection point, position informationindicating the distance from the sensor 10G to the detection point andthe direction of the detection point with reference to the sensor 10G.

When the sensor 10G is a LIDAR sensor as described above, the sensor 10Gmay calculate the position information (the emission direction and thedistance) from the emission direction of the laser light and the elapsedtime from the emission of the laser light to the reception thereof. Inthis case, the acquisition function 10K may acquire the positioninformation of each detection point from the sensor 10G (LIDAR sensor).

As described above, the sensor 10G is configured to obtain observationinformation on each plane included in the surface of the object 30 insome cases. In such a case, the acquisition function 10K may acquireposition information of a detection point by projecting each of thedifferent planes indicated by the observation information onto thehorizontal plane. The sensor 10G is also configured to obtain imagescaptured in a plurality of image capturing directions different fromeach other in some cases. In such a case, the acquisition function 10Kmay estimate three-dimensional position information of each detectionpoint by restructuring depth information (distance information) on thedetection point based on images captured in a plurality of imagecapturing directions and acquired from the sensor 10G. The sensor 10Gincludes a plurality of sensors 10G in some cases. In such a case, theacquisition function 10K may integrate observation information obtainedby these sensors 10G, and may use the integrated observation informationto acquire position information of each detection point.

FIGS. 4A to 4D are explanatory diagrams of processing executed by theprocessing circuit 10A. FIG. 4A is an explanatory diagram of acquisitionof position information by the acquisition function 10K. As illustratedin FIG. 4A, the sensor 10G emits light around the sensor 10G andreceives light reflected by the objects 30 (objects 30A to 30E) aroundthe sensor 10G. In this manner, the sensor 10G obtains observationinformation on each of detection points 32 (for example, detectionpoints 32 ₁ to 32 ₂₇) corresponding to reflection points on the objects30 (objects 30A to 30E). The positions of the objects 30, the number ofobjects 30, the number of detection points 32, and the positions of thedetection points 32 are exemplary, and the present embodiment is notlimited to the configuration illustrated in FIGS. 4A to 4D.

The acquisition function 10K acquires position information of thedetection points 32 (for example, the detection points 32 ₁ to 32 ₂₇)from the observation information.

The description continues with reference to FIG. 2. The categorizationfunction 10L is an exemplary categorizer. The categorization function10L clusters the detection points 32, the position information of whichis acquired by the acquisition function 10K, into detection point groupsrepresenting the object 30. Specifically, the categorization function10L categorizes the detection points 32 into a plurality of categories.The categories include at least the object 30. The categories mayinclude an element other than the object 30.

FIG. 4B is an explanatory diagram of the categorization by thecategorization function 10L. The categorization function 10L clustersthe detection points 32 into detection point groups B representing theobject 30 based on the position information of each detection point 32.

For example, assume that the sensor 10G is a two-dimensional LIDARsensor. In this case, the categorization function 10L applies a numberto (orders) each detection point 32 sequentially in the circumferentialdirection of a two-dimensional circle along the horizontal plane havingthe sensor 10G at its center.

Then, in an order of the applied number, the information processingdevice 10 determines whether each detection point 32 represents the sameobject 30 to which the previous detection points 32 represents. Forexample, if a difference between a distance (distance to the sensor 10G)indicated by the position information of the previous detection points32 and a distance indicated by the position information of the detectionpoint 32 to be processed is equal to or smaller than a threshold, thecategorization function 10L determines that these detection points 32represents the same object 30. Then, the categorization function 10Lrepeats the determination on the detection points 32 sequentially in thecircumferential direction until the detection point 32 to be processedand its previous detection point 32 are determined not to represent thesame object 30. Then, a group of the detection points 32 determined torepresent the same object 30 is clustered as a detection point group Brepresenting one object 30.

In the example illustrated in FIGS. 4A and 4B, this categorizationprocessing clusters the detection points 32 (detection points 32 ₁ to 32₂₇) into one or more detection point groups B (detection point groups B1to B5) representing the respective objects 30.

A method of the categorization by the categorization function 10L is notlimited to the above-described method. Specifically, in the abovedescription, the detection points 32 are ordered sequentially in thecircumferential direction of a two-dimensional circle having its centerat the sensor 10G, and then the categorization processing is performed.However, a method of the ordering of the detection points 32 is notlimited to this method.

The criterion of the determination of whether the detection points 32represent the same object 30 is not limited to the above-describedcriterion. For example, the categorization function 10L may use positioncoordinates indicating the relative positions of the detection points 32with respect to the sensor 10G and the absolute positions of thedetection points 32 as criteria of the determination. Alternatively, thecategorization function 10L may determine whether distances from aplurality of detection points 32 determined to represent the same object30 to a detection point 32 to be determined next are equal to or smallerthan a threshold so as to determine whether the detection point 32 to bedetermined next represents the same object 30. Alternatively, thecategorization function 10L may determine whether detection points 32represent the same object 30 based on a reflection intensity of lightincluded in the observation information of the detection points 32.

The description continues with reference to FIG. 2. The shape fittingfunction 10M is an exemplary shape fitter. The shape fitting function10M applies a predetermined shape model to a detection point group Bbased on position information of each detection point 32 belonging tothe detection point group B. In other words, the shape fitting function10M applies a shape model M to each detection point group B based onposition information of each of detection points 32 clustered as thecorresponding object 30.

Specific description will be made below with reference to FIGS. 4A to4D. FIG. 4C is an explanatory diagram illustrating exemplary applicationof the shape model M by the shape fitting function 10M.

For example, the shape fitting function 10M applies the shape model M toeach detection point group B by using a shape model of one kind, whichis determined in advance, through adjustments such as change ofcoordinates, angle change (barycenter rotation) in a long-sidedirection, and deformation of the lengths of sides.

The following describes an example in which the shape fitting function10M prepares a rectangle as the kind of the shape model for applicationof the shape model M to an object 30 (for example, an automaticfour-wheel vehicle) around the sensor 10G. The shape fitting function10M applies a rectangle as the shape model to detection point groups B(detection point groups B1 to B5) (refer to FIG. 4B).

For example, the shape fitting function 10M has, as variables of arectangle as the shape model, the coordinates of the rectangle, an anglein one reference direction, indicating the posture of the rectangle, andthe lengths of sides of the rectangle. Then, for each detection pointgroup B representing one object 30 and projected onto the horizontalplane, the shape fitting function 10M sets initial values to thevariables of the rectangle as a prepared shape model. In addition, theshape fitting function 10M slightly changes the variables from theinitial values and performs scanning to define, as the shape model, onerectangle for which the variables minimize an evaluating function.Accordingly, the defined shape model applies as the shape model M of thedetection point group B.

For example, when the kind of the shape model is a rectangle, thecoordinates, the angle (angle of the long-side direction relative to onecoordinate axis), and the lengths of sides of a rectangle having aminimum area to enclose a detection point group B to be processed areset as the initial values of the variables of the shape model. A methodof setting the initial values is not limited to this method. Forexample, the shape fitting function 10M may employ a method of detectinga straight line by performing a Hough transform on the detection pointgroup B and setting the straight line to be one side of a rectangle asan initial shape model.

The evaluating function uses, for example, the sum of a shortestdistance between each detection point 32 belonging to a detection pointgroup B and a side of an applied shape model.

The shape fitting function 10M adjusts the variables of a shape model todefine one shape model for which the evaluating function has a minimumvalue. Then, the shape fitting function 10M uses the defined shape modelas the shape model M to be applied to the corresponding detection pointgroup B. In this manner, the shape fitting function 10M applies theshape model M to the detection point group B representing an object 30.

The shape fitting function 10M may use, as the evaluating function, alongest distance among the above-described shortest distances of aplurality of detection points 32 belonging to a detection point group B.Specifically, the shape fitting function 10M may calculate theevaluating function by using one detection point 32 for which a shortestdistance from a side of an applied initial shape model is largest amongthe detection points 32 belonging to the detection point group B.

Alternatively, the shape fitting function 10M may use, as the evaluatingfunction, each detection point 32 weighted in accordance with positioninformation of the detection point 32.

For example, the shape fitting function 10M may fit the shape model Minto a detection point group B representing a certain object 30 by usinga plurality of detection points 32 belonging to a detection point groupB representing the object 30, and one or more detection points 32belonging to a detection point group B representing another object 30.

In this case, the shape fitting function 10M uses, as the evaluatingfunction, the number of detection points 32 (referred to as otherdetection points 32) included in a detection point group B to beprocessed and also belonging to another detection point group B, or thenumber of points at which a straight line connecting the sensor 10G andeach of the other detection points 32 intersects with an initial shapemodel applied to the detection point group B to be processed.

The shape fitting function 10M may use the sum of these plurality ofkinds of evaluating functions as the evaluating function used forapplication of the shape model M.

The shape fitting function 10M may fit the shape model M into adetection point group B by using another method.

For example, the shape fitting function 10M may determine the type of anobject 30 from a detection point group B, select the shape model Mrelated to the type of the object 30, and fit the shape model M into thedetection point group B.

For example, the shape fitting function 10M sets in advance the shapemodel M different for each type of an object 30. Specifically, the shapefitting function 10M sets in advance a circular shape model M for thetype “person” of the object 30, a rectangular shape model M for the type“automatic four-wheel vehicle” of the object 30, and a linear shapemodel M for the type “guardrail” of the object 30.

Then, the shape fitting function 10M may select the shape model Mrelated to the type of the object 30 determined from the detection pointgroup B. In this manner, the shape fitting function 10M can fit adifferent type of the shape model M in accordance with a result ofdetermination of the type of the object 30.

The shape fitting function 10M may fit the shape model M into adetection point group B by additionally using the “possible size range”related to the type of an object 30 in the shape model managementinformation 10J (refer to FIG. 3). In this case, the “possible sizerange” may be determined in advance for each type of the object 30. Forexample, when the shape of the shape model M is a rectangle, the size ofthe rectangle differs in accordance with the type of the related object30. Thus, the “possible size range” may employ an average size of thetypes (a bicycle, an automatic four-wheel vehicle, or a bus, forexample) of the corresponding object 30.

Then, the information processing device 10 may add the size (forexample, the length of a side of a rectangle) of the shape model as avariable of the application to the detection point group B, set a searchrange in which the variable of the size can be changed as the “possiblesize range” related to the type of the object 30, and fit the shapemodel M into the detection point group B.

Accordingly, for example, as illustrated in FIGS. 4B and 4C, a shapemodels M1 to M5 are applied to the respective detection point groups B1to B5.

The description continues with reference to FIG. 2. The specificationfunction 10N is an exemplary specifier. The specification function 10Nspecifies the target point T of the object 30 based on the shape model Mapplied by the shape fitting function 10M. Specifically, thespecification function 10N specifies, based on the shape model M, thetarget point T of the object 30 corresponding to the detection pointgroup B to which the shape model M is fitted.

In the present embodiment, the specification function 10N specifies, asthe target point T of the object 30, one of candidate points set to theshape model M in advance.

FIG. 4D is an explanatory diagram illustrating exemplary specificationof the target point T by the specification function 10N. For example,assume that, for each of the shape models M1 to M5, four apexes(candidate points C1 to C4) of the rectangular shape model M are set ascandidate points C related to the shape model M in advance. In thiscase, for each of the shape models M (shape models M1 to M5) applied forthe respective detection point groups B, the specification function 10Nspecifies, as the target point T, one of candidate points C (candidatepoints C1 to C4) of the shape model M.

The specification function 10N may read, from the shape model managementinformation 10J, the candidate points C related to the shape model Mfitted by the shape fitting function 10M, and use the candidate points Cfor specification of the target point T.

Specifically, the specification function 10N sets the candidate points Crelated to the shape model M in advance. In the present embodiment, thespecification function 10N may set the candidate points C in advance byregistering the shape model M and the candidate points C in associationwith each other to the shape model management information 10J (refer toFIG. 3) in advance.

The specification function 10N specifies, as the target point T, one ofthe candidate points set in advance to the applied shape model M.

For example, the specification function 10N specifies, as the targetpoint T, a candidate point C closest to the position of the sensor 10Gamong the candidate points C set in advance to the shape model M.

This position of the sensor 10G may be the position of the sensor 10Gwhen the observation information is acquired. The specification function10N may specify the position of each candidate point C based on positioninformation of each detection point 32 and a relative positionalrelation between each candidate point C in the shape model M and eachdetection point 32 belonging to a detection point group B. Then, thespecification function 10N specifies, as the target point T, a candidatepoint C at a position closest to the position of the sensor 10G amongthe candidate points C set in advance to the shape model M.

As described above, the shape model M is fitted to each detection pointgroup B of detection points 32 representing an object 30. Theacquisition function 10K acquires position information of each detectionpoint 32. Thus, the specification function 10N can acquire the relativepositional relation between each candidate point C set to the shapemodel M and each detection point 32 belonging to the detection pointgroup B.

The following description will be made with reference to FIG. 4D. Forexample, assume that candidate points C (candidate points C1 to C4) areset to the shape model M1 fitted to the detection point group B1 (referto FIG. 4B) of the object 30A (refer to FIG. 4D). In this case, thespecification function 10N specifies, as the target point T of theobject 30A, the candidate point C4 closest to the position of the sensor10G among the candidate points C (candidate points C1 to C4) of theshape model M1.

Similarly for each of the shape models M2 to M5 fitted to the respectivedetection point groups B2 to B5, the specification function 10Nspecifies a candidate point C closest to the position of the sensor 10G(in the example illustrated in FIG. 4D, the candidate point C1 of theshape model M2, the candidate point C2 of the shape model M3, thecandidate point C2 of the shape model M4, and the candidate point C4 ofthe shape model M5) among candidate points C as the target point T ofthe object 30 (objects 30B to 30E) related to each of the shape models M(shape models M2 to M5).

The specification function 10N may specify, as the target point T of theobject 30, a candidate point C closest to detection points 32 belongingto a detection point group B to which the shape model M is applied amongcandidate points C set in advance to a shape model M. Specifically, thespecification function 10N may specify a candidate point C, closest towhich a detection point 32 exists, among the candidate points C set inadvance to the shape model M, as the target point T of the object 30.

Alternatively, the specification function 10N may specify, as the targetpoint T of the object 30, a candidate point C having a highestdistribution rate of detection points 32 belonging to a detection pointgroup B among candidate points C set in advance to the shape model M.

The distribution rate of detection points 32 represents a ratio ofdetection points 32 existing around a candidate point C. Thedistribution rate of detection points 32 indicates a larger value for alarger number of detection points 32 positioned closer to the candidatepoint C. For example, the distribution rate of detection points 32 atthe candidate point C is expressed as the sum of values applied todetection points 32 positioned in a predetermined range from thecandidate point C, where a larger value is applied to a detection point32 positioned closer to the candidate point C.

Then, a candidate point C having a highest distribution rate ofdetection points 32 among the candidate points C of the shape model Mfitted to the detection point group B of the object 30 may be specifiedas the target point T of the object 30.

In other words, the specification function 10N can specify the targetpoint T of the object 30 based on the distribution rate by specifyingthe target point T by using, among detection points 32 belonging to thedetection point group B, the number of detection points 32 included inthe predetermined range from a candidate point C and the distance fromthe candidate point C to a detection point 32 closest to the candidatepoint C.

The fitness of the shape model M to the candidate point C is morerelevant for a higher distribution rate of detection points 32. Thus,the specification function 10N can achieve an improved accuracy ofspecification of the target point T of the object 30 by specifying acandidate point C having a highest distribution rate of detection points32 as the target point T. In addition, any unstable candidate point Cwith a few surrounding detection points 32 among a plurality ofcandidate points C set to the shape model M can be excluded as thetarget point T. Thus, the specification function 10N can accuratelyspecify the target point T of the same object 30 in observationinformation of observations at different timings.

The output control function 10P is an exemplary output unit. The outputcontrol function 10P outputs the output information indicating thetarget point T of an object 30 from the output circuit 10C.Alternatively, the output control function 10P may store the outputinformation in the storage circuit 10B. Alternatively, the outputcontrol function 10P may output the output information to any otherprocessing functional component (for example, a function that performscollision determination or motion prediction). Alternatively, the outputcontrol function 10P may perform at least two of the storage of theoutput information in the storage circuit 10B, the outputting of theoutput information, and the outputting of the output information to anyother processing functional component.

As described above, the output information includes information relatedto the target point T of the object 30. Specifically, the outputinformation includes at least one of information indicating the positionof the target point T of the object 30, and the moving direction, moveddistance, and moving speed of the object 30 derived based on the targetpoint T.

For example, the output control function 10P outputs, from the outputcircuit 10C, the output information related to the target point T of theobject 30 represented by each detection point group B clustered by thecategorization function 10L.

Accordingly, the communication circuit 10D of the output circuit 10Ctransmits the output information related to the target point T to, forexample, an external device. For example, the output information isdisplayed on the display 10E of the output circuit 10C. For example, thespeaker 10F of the output circuit 10C outputs sound in accordance withthe output information. The sound in accordance with the outputinformation may be sound indicating the output information, or warningby sound in accordance with the output information.

The following describes the procedure of processing executed by theprocessing circuit 10A. FIG. 5 is a flowchart of an exemplary procedureof the processing executed by the processing circuit 10A.

First, the acquisition function 10K acquires the observation informationfrom the sensor 10G (step S100). Subsequently, the acquisition function10K acquires position information of each detection point 32 from theobservation information acquired at step S100 (step S102).

Subsequently, the categorization function 10L clusters detection points32, the position information of which is acquired at step S102, into adetection point group B representing an object 30 (step S104).Subsequently, the shape fitting function 10M fits a shape model M to thedetection point group B clustered at step S104 (step S106). If the shapefitting function 10M clusters the detection points 32 into detectionpoint groups B in the processing at step S104, the shape fittingfunction 10M fits shape models M to the respective detection pointgroups B.

Subsequently, the specification function 10N specifies the target pointT of the object 30 based on the shape model M fitted at step S106 (stepS108). If the shape fitting function 10M fits shape models M to therespective detection point group B in the processing at step S106, thespecification function 10N specifies the target point T for each ofobjects 30 corresponding to the respective shape models M.

Subsequently, the specification function 10N outputs the outputinformation related to the target point T specified at step S108 fromthe output circuit 10C (step S110).

Subsequently, the processing circuit 10A determines whether to end theprocessing (step S112). For example, the processing circuit 10A performsthe determination at step S112 by determining whether a signalindicating end of the processing has been received from the input device10H. If the determination at step S112 is negative (No at step S112),the present routine returns to the above-described step S100. If thedetermination at step S112 is positive (Yes at step S112), the presentroutine is ended.

Through execution of the above-described processing, each time thesensor 10G acquires observation information, the processing circuit 10Afits a shape model M to a detection point group B based on positioninformation of each detection point 32 acquired from the observationinformation, and specifies the target point T of an object 30 based onthe shape model M. Thus, the target point T of the object 30 can bespecified for a plurality of pieces of observation information differentfrom each other in, for example, the observation time.

FIGS. 6A to 6C are explanatory diagrams illustrating exemplaryspecification of the target point T by the processing circuit 10Aaccording to the present embodiment. Assume that the object 30Arepresented by a shape model M1 is moving in a direction (the directionof arrow X) away from the sensor 10G. Then, assume that the sensor 10Gacquires observation information at each of time t1, time t2, time t3,and time t4.

Then, assume that the processing circuit 10A executes the processing atthe above-described steps S100 to S112 at each acquisition of theobservation information at each of time t1, time t2, time t3, and timet4.

In this case, the acquisition function 10K acquires position informationof detection points 32 (detection points 32 ₁ to 32 ₅) representing theobject 30A at each of time t1, time t2, time t3, and time t4 (refer toFIG. 6A). Then, the categorization function 10L and the shape fittingfunction 10M fit the shape model M1 into the detection point group B1representing the object 30A at each of time t1, time t2, time t3, andtime t4 (refer to FIGS. 6A and 6B).

The specification function 10N specifies, as the target point T, one ofcandidate points C of the shape model M1 fitted at each of time t1, timet2, time t3, and time t4. The example illustrated in FIGS. 6A to 6Cillustrates a case in which the specification function 10N specifies, asthe target point T, a candidate point C4 closest to the sensor 10G amongcandidate points C of the shape model M1 fitted at each of time t1, timet2, time t3, and time t4.

Specifically, the specification function 10N specifies the position ofeach candidate point C (candidate points C1 to C4) set to the appliedshape model M1. In the example illustrated in FIGS. 6A to 6C, thespecification function 10N specifies the positions of four apexes of therectangular shape model M1.

Subsequently, the specification function 10N specifies the position ofthe sensor 10G at each time (time t1, time t2, time t3, or time t4) atwhich the observation information is acquired. In the presentembodiment, the specification function 10N uses the position of theinformation processing device 10 as the position of the sensor 10G,assuming that the sensor 10G is installed in the information processingdevice 10. Alternatively, the relative position of the sensor 10G toeach detection point 32 may be used as the position of the sensor 10G.

Then, the specification function 10N calculates the distance between thesensor 10G and each of candidate points C (candidate points C1 to C4)included in the shape model M1. The specification function 10Nspecifies, as the target point T, the candidate point C4 having ashortest distance to the sensor 10G among the candidate points C1 to C4set to the shape model M1, for each time (time t1, time t2, time t3, ortime t4) at which the observation information is acquired.

In this manner, the specification function 10N specifies, as the targetpoint T, one of candidate points C set to the shape model M1 in advance.Thus, the target point T of the object 30 can be reliably specified whenthe observation information is acquired at different times.

As described above, the information processing device 10 according tothe present embodiment includes the acquisition function 10K, thecategorization function 10L, the shape fitting function 10M, and thespecification function 10N. The acquisition function 10K acquiresposition information of each of detection points 32. The categorizationfunction 10L clusters detection points 32 into a detection point group Brepresenting an object 30. The shape fitting function 10M fits apredetermined shape model M into the detection point group B based onthe position information of each clustered detection point 32 belongingto the detection point group B. The specification function 10N specifiesa target point T of the object 30 based on the shape model M.

In this manner, the information processing device 10 according to thepresent embodiment specifies the target point T of the object 30 basedon the shape model M fitted into the detection point group Brepresenting the object 30.

Thus, the information processing device 10 according to the presentembodiment can accurately specify the target point T of the object 30.

In the present embodiment, the specification function 10N specifies, asthe target point T of the object 30, one of candidate points set inadvance to the shape model M. Thus, in the information processing device10 according to the present embodiment, the same position on the object30 can be specified as the target point T. Specifically, when the sameobject 30 is included in a plurality of pieces of the observationinformation different from each other in, for example, a time or anobservation direction, the same position on the object 30 can bespecified as the target point T.

The target point T of the object 30 specified by the informationprocessing device 10 according to the present embodiment can be used toderive, for example, the position of the object 30, the moving distanceof the object 30, and the moving speed of the object 30.

For example, as illustrated in FIG. 6C, the specification function 10Nmay calculate at least one of the moving direction, the moving distance,and the moving speed of the object 30A using the target point T of theobject 30A specified at each of time t1, time t2, time t3, and time t4.

In this case, the specification function 10N calculates, for example,the direction of a straight line (refer to the direction of arrow X inFIG. 6D) connecting the target points T of the object 30A specified atdifferent times as the moving direction of the object 30A. Thespecification function 10N also calculates, for example, the distancebetween the target points T specified at different times as the movingdistance of the object 30A between the times. The specification function10N also calculates the moving speed of the object 30A using, forexample, the distance between the target points T specified at differenttimes and the interval between the times.

Thus, in addition to the above-described effect, the informationprocessing device 10 according to the present embodiment can highlyaccurately derive at least one of the position, the moving distance, andthe moving speed of the object 30 using the target points T of theobject 30 specified by the information processing device 10 according tothe present embodiment.

Different positions on the same object 30 are specified as the targetpoints T in some cases depending on a positional relation between theobject 30 and the sensor 10G.

FIG. 7 is an explanatory diagram of a case in which different positionson the object 30 are specified as target points T. As illustrated inFIG. 7, assume that the object 30A moves toward the sensor 10G and thenmoves away from the sensor 10G (refer to the direction of arrow X inFIG. 7).

Then, assume that the specification function 10N specifies, as thetarget point T, a candidate point C closest to the sensor 10G amongcandidate points C (candidate points C1 to C4) set to the shape model M1applied to the detection point group B1 of the object 30A. In this case,the target point T of the object 30A specified based on observationinformation observed at time t1 is a candidate point C1 in the shapemodel M1 applied to the detection point group B1 of the object 30A. Thetarget point T of the object 30A specified based on observationinformation observed at time t2 is a candidate point C4 in the shapemodel M1 fitted into the detection point group B1 of the object 30A.

In this case, error occurs in calculation of the moved distance, themoving direction, and the moving speed of the object 30A for the movingamount of the object 30A, using the candidate point C4 specified as thetarget point T at time t2 and the candidate point C1 specified as thetarget point T at time t1.

Thus, the specification function 10N preferably employs a methoddescribed below when at least one of the moving distance using thespecified target point T, the moving direction, and the moving speed ofthe object 30 (in FIG. 7, the object 30A) is derived.

Specifically, in this case, the specification function 10N may derivethe moving distance, the moving direction, and the moving speed usingcandidate points C at positions corresponding to each other in shapemodels M1 applied to the object 30A at different times. The candidatepoints C at the corresponding positions indicate candidate points Chaving the same positional relation in shape models M (shape models M1,for example) applied to the same object 30 (in this example, the object30A) at different times.

In the example illustrated in FIG. 7, the specification function 10Nderives at least one of the moving distance, the moving direction, andthe moving speed of the object 30A using the target point T as candidatepoint C1 at time t1, and candidate point C1 as the candidate point C attime t2 corresponding to candidate point C1 at time t1.

In this manner, the information processing device 10 according to thepresent embodiment can accurately derive at least one of the movingdistance, the moving direction, and the moving speed of an object 30 inaddition to the above-described effect.

When deriving at least one of the moving distance, the moving direction,and the moving speed of the object 30A, the specification function 10Nmay use, as the moving amount of the object 30A, the sum of the distancebetween the target point T (candidate point C1) at time t1 and thetarget point T (candidate point C4) at time t2, and the length of theobject 30A in the moving direction. A positional relation betweencandidate point C1 and candidate point C4 in the shape model M may beused in place of the length of the object 30A in the moving direction.

The present embodiment describes the case in which the specificationfunction 10N specifies, as the target point T, any one of a candidatepoint C closest to the position of the sensor 10G, a candidate point Chaving a highest distribution rate of detection points 32, and acandidate point C closest to detection points 32 among candidate pointsC set to the shape model M in advance. However, the specificationfunction 10N may specify, as the target point T, one of candidate pointsC set to the shape model M in advance, and a method of thisspecification is not limited.

The specification function 10N may use a combination of a plurality ofspecification methods for the specification of the target point T. Forexample, the specification function 10N calculates, as one referencevalue, the sum of reference values of specification methods. Then, thespecification function 10N may determine candidate points C set to theshape model M based on the reference value, and specify, as the targetpoint T, a candidate point C determined to be most relevant.Alternatively, the specification function 10N may perform determinationprocessing with a threshold set to each of specification methods andspecify, as the target point T, a candidate point C for which a largepart (or all) of the thresholds are satisfied.

In this manner, the specification function 10N specifies the targetpoint T through a combination of a plurality of specification methods,and thus the information processing device 10 can more accuratelyspecify the target point T.

For example, as illustrated in FIG. 7, during a time period includingtimes (time t1 and time t2) between which a candidate point C specifiedas the target point T from the shape model M1 applied to the object 30Achanges, the specification of the target point T with one specificationmethod causes variation of a candidate point C specified as the targetpoint T in some cases. However, when the specification function 10Nspecifies the target point T through a combination of a plurality ofspecification methods, such a variation can be avoided.

Second Embodiment

FIG. 8 is a block diagram of an exemplary configuration of aninformation processing device 14. In the present embodiment, theinformation processing device 14 specifies the target point T by amethod different from that used by the information processing device 10according to the first embodiment.

The following describes the configuration of the information processingdevice 14 in detail.

The information processing device 14 specifies the target point T of theobject 30. The information processing device 14 is, for example, adedicated or generalized computer. The information processing device 14includes a processing circuit 14A, the storage circuit 10B, the outputcircuit 10C (the communication circuit 10D, the display 10E, and thespeaker 10F), the sensor 10G, and the input device 10H.

The information processing device 14 is same as the informationprocessing device 10 according to the first embodiment except forincluding the processing circuit 14A in place of the processing circuit10A.

The processing circuit 14A includes the acquisition function 10K, thecategorization function 10L, a searcher 14R, a shape fitting function14M, a specification function 14N, and the output control function 10P.The processing circuit 14A is same as the processing circuit 10A exceptfor including the shape fitting function 14M and the specificationfunction 14N in place of the shape fitting function 10M and thespecification function 10N, and further including the searcher 14R.

In the present embodiment, the processing circuit 14A specifies thetarget point T of the object 30 at a timing (first timing) from thetarget point T of the same object 30 at another timing (second timing).

The first timing and the second timing are different from each other inat least one of the timing of acquisition of the observation informationby the sensor 10G and the position of the sensor 10G. When theacquisition timing differs between the first and the second timings, thesecond timing may be earlier than the first timing or later than thefirst timing. When the position of the sensor 10G differs between thefirst and the second timings, it is treated as if the informationprocessing device 14 includes a plurality of sensors 10G installed atdifferent positions. Then, one of pieces of observation informationacquired from the respective sensors 10G is used as observationinformation at the first timing, and observation information acquiredfrom the other sensors 10G is used as observation information at thesecond timing.

When the second timing is later than the first timing (in other words,in the future of the first timing), the processing circuit 14A may firstacquire a plurality of pieces of observation information correspondingto the respective timings, and then specify the target point T of theobject 30 based on the observation information of the respectivetimings.

The processing circuit 14A according to the present embodiment furtherincludes the searcher 14R. The processing circuit 14A includes the shapefitting function 14M and the specification function 14N in place of theshape fitting function 10M and the specification function 10N accordingto the first embodiment. The acquisition function 10K, thecategorization function 10L, and the output control function 10P aresame as those in the first embodiment.

FIGS. 9A and 9B are explanatory diagrams of processing executed by theprocessing circuit 14A.

The searcher 14R searches for a detection point group B representing anobject 30 at a second timing t2′, which corresponds to a detection pointgroup B representing the same object 30 at a first timing t1′.

For example, assume that the acquisition function 10K acquires positioninformation of detection points 32 c to 32 f as position information ofdetection points 32 representing an object 30F at the second timing t2′as illustrated in FIG. 9A. In addition, assume that the categorizationfunction 10L clusters these detection points 32 c to 32 f into adetection point group Bb representing the object 30F.

The acquisition function 10K acquires position information of detectionpoints 32 (detection points 32 a and 32 b) at the first timing t1′.Then, the categorization function 10L clusters these detection points 32(detection points 32 a and 32 b) into a detection point group Barepresenting the object 30F.

Then, the searcher 14R performs, for each detection point group Bclustered by the categorization function 10L, a search for a detectionpoint group B representing the same object 30 at the second timing t2′.

For example, the searcher 14R stores the detection point groups B at thesecond timing t2′. Then, the searcher 14R receives the detection pointgroup Ba at the first timing t1′ from the categorization function 10L,and searches the detection points group B at the second timing t2′ for adetection point group B (detection point group Bb) representing theobject 30F identical to the object 30F represented by the detectionpoint group Ba.

For example, the searcher 14R performs, for each detection point 32included in the detection point group Ba at the first timing t1′, asearch for the corresponding detection point 32 at the second timingt2′. Then, the searcher 14R searches the detection point groups B at thesecond timing t2′ for a detection point group B including a largestnumber of detection points 32 corresponding detection points 32 includedin the detection point group Ba at the first timing t1′, as thedetection point group Bb representing the same object 30F.

Specifically, the searcher 14R searches for a detection point 32existing at a closest position at the second timing t2′ as acorresponding detection point 32 by using position information of thedetection points 32 included in the detection point group Ba at thefirst timing t1′. Then, a detection point group B including a largestnumber of corresponding detection points 32 at the second timing t2′ issearched as the detection point group Bb representing the same object30F at the second timing t2′.

The searcher 14R may search for a detection point 32 at the secondtiming t2′ corresponding to each detection point 32 included in thedetection point group Ba at the first timing t1′ by another method.

For example, the searcher 14R calculates, for each detection point 32included in the detection point group Ba at the first timing t1′, afeature amount indicating distribution of other surrounding detectionpoints 32. Then, the searcher 14R may search for a detection point 32having a closest feature amount at the second timing t2′ as acorresponding detection point 32.

The searcher 14R may search for the detection point group Bbrepresenting the same object 30F at the second timing t2′, whichcorresponds to the detection point group Ba at the first timing t1′based on a difference (for example, an elapsed time) between the firsttiming t1′ and the second timing t2′ and the moving direction and themoving amount of the object 30F at the second timing t2′.

In this manner, the searcher 14R searches detection points 32 at thesecond timing t2′ for the detection point group Bb representing theobject 30F identical to the object 30F represented by the detectionpoint group Ba at the first timing t1′.

The searcher 14R may perform association for each detection point groupB representing the same object 30 by using a detection point group Bclustered at the first timing t1′ and a detection point group Bclustered at the second timing t2′. In this case, the searcher 14R maysearch for a corresponding detection point group B by using the movingdistance of each detection point 32 belonging to a detection point groupB, and the feature of each detection point 32 belonging to the detectionpoint group B.

The shape fitting function 14M is an exemplary shape fitter. The shapefitting function 14M applies the shape model M to each of the detectionpoint group Ba at the first timing t1′ and the corresponding detectionpoint group Bb at the second timing t2′. Similarly to the shape fittingfunction 10M according to the first embodiment, the shape fittingfunction 14M may fit the shape model M into the detection point group B.

In an example illustrated in FIGS. 9A and 9B, the shape fitting function14M fits a shape model M into the detection point group Ba representingthe object 30F at the first timing t1′. The shape fitting function 14Malso fits a shape model M′ into the detection point group Bbrepresenting the same object 30F at the second timing t2′ (refer to FIG.9B).

Then, the specification function 14N specifies the target point T of theobject 30F at the first timing t1′ based on a target point T′ set basedon the shape model M fitted into the detection point group Ba at thefirst timing t1′ and the shape model M′ applied to the detection pointgroup Bb at the second timing t2′ (refer to FIG. 9B).

Specifically, first, the specification function 14N fits the shape modelM′ to the detection point group Bb at the second timing t2′, andspecifies one of candidate points C of the shape model M′ as the targetpoint T′. The fitting of the shape model M′ and the specification of thetarget point T′ are performed by methods same as those in the firstembodiment.

When the target point T′ is already specified for the shape model M′fitted into the detection point group Bb at the second timing t2′, thespecification function 14N may specify the target point T′ by readingthe specified target point T′.

Then, the specification function 14N specifies the target point T of thedetection point group Ba from among candidate points C (C1 to C4) set tothe shape model M fitted into the detection point group Ba at the firsttiming t1′, based on the target point T′ of the shape model M′ at thesecond timing t2′.

In the example illustrated in FIG. 9B, the specification function 14Nassociates candidate points C of the shape model M and the shape modelM′, assuming that the object 30F is moving in a direction (the directionof arrow X) away from the sensor 10G. Accordingly, in the exampleillustrated in FIG. 9B, the specification function 14N specifies, as thetarget point T of the detection point group Ba, the candidate point C4at a position corresponding to the target point T′ (the candidate pointC4) of the shape model M′ at the second timing t2′ among the candidatepoints C (C1 to C4) set to the shape model M at the first timing t1′.

In this manner, in the present embodiment, the specification function14N specifies the target point T of the same object 30 at the firsttiming t1′ by using the target point T′ specified at another timing (thesecond timing t2′).

Thus, the information processing device 14 according to the presentembodiment can reliably specify the target point T of the object 30, forexample, when a small number of detection points 32 are included in adetection point group B representing an object 30 at the first timingt1′, or when each of candidate points C of an applied shape model Mexists at a position distant from the sensor 10G.

Observation information used as the second timing t2′ preferably has alarger number of detection points 32 belonging to a detection pointgroup B than the number of detection points 32 belonging to thedetection point group B, which is derived from observation informationof another timing. Observation information used as the second timing t2′preferably has a closer distance from the sensor 10G to the object 30than observation information of another timing.

Accordingly, the specification function 14N can more accurately specifythe target point T of the object 30.

In the above description, the searcher 14R stores detection point groupsB at the second timing t2′ and searches the detection point groups B atthe second timing t2′ for a detection point group B (the detection pointgroup Bb) representing the object 30F identical to the object 30Frepresented by the detection point group Ba at the first timing t1′.

However, the processing circuit 14A may search shape models M fittedinto detection point groups B at the second timing t2′ for a shape modelM′ of the same object 30F at the second timing t2′, which corresponds toa shape model M fitted into the detection point group Ba at the firsttiming t1′, and may use the shape model M′ for the specification of thetarget point T.

The present embodiment describes the case in which the target point T isspecified by using the two timings of the first timing t1′ and thesecond timing t2′. However, the processing circuit 14A may specify thetarget point T by using a plurality of the second timings t2′ for thefirst timing t1′.

The processing circuit 14A may predict movement of the object 30F at thefirst timing t1′ from the moving direction and the moving speed of thesame object 30F at the second timing t2′. Then, the processing circuit14A may specify the target point T by using a shape model M at apredicted position and a shape model M fitted into the detection pointgroup Ba at the first timing t1′.

The following describes an exemplary procedure of processing executed bythe processing circuit 14A. FIG. 10 is a flowchart of an exemplaryprocedure of the processing executed by the processing circuit 14A. Inthe process illustrated in FIG. 10, the second timing t2′ is earlierthan (in the past of) the first timing t1′.

First, the acquisition function 10K acquires observation information ata first timing t1′ from the sensor 10G (step S200). This first timingt1′ may be updated with the timing of each acquisition of theobservation information by the sensor 10G.

Subsequently, the acquisition function 10K acquires position informationof each detection point 32 from the observation information at the firsttiming t1′ acquired at step S200 (step S202).

Subsequently, the categorization function 10L clusters detection points32, the position information of which is acquired at step S202, into adetection point group B representing an object 30 (step S204). Thefollowing description assumes that, at step S204, the categorizationfunction 10L clusters detection points 32 (detection points 32 a and 32b) into a detection point group Ba representing an object 30F.

Subsequently, the searcher 14R searches for a corresponding detectionpoint group Bb representing the object 30F at the second timing t2′(step S206). Subsequently, the shape fitting function 14M fits a shapemodel M into a detection point group Ba representing the object 30F atthe first timing t1′ (step S208). The shape fitting function 14M alsofits a shape model M′ into the detection point group Bb representing thesame object 30F at the second timing t2′ (step S208).

Subsequently, the specification function 14N specifies, as a targetpoint T′, one of candidate points C of the shape model M′ applied to thedetection point group Bb at the second timing t2′ (step S210).

Subsequently, the specification function 14N specifies, as the targetpoint T of the object 30F at the first timing t1′, a candidate point Cat a position corresponding to the target point T′ specified at stepS210 among candidate points C (C1 to C4) set to the shape model M fittedinto the detection point group Ba at the first timing t1′ (step S212).

Subsequently, the specification function 14N outputs output informationindicating the target point T specified at step S212 (step S214).

Subsequently, the processing circuit 14A determines whether to end theprocessing (step S216). For example, the processing circuit 14A performsthe determination at step S216 by determining whether a signalindicating end of the processing has been received from the input device10H. If the determination at step S216 is negative (No at step S216),the present routine returns to the above-described step S200. If thedetermination at step S216 is positive (Yes at step S216), the presentroutine is ended.

As described above, in the information processing device 14 according tothe present embodiment, the searcher 14R searches for the detectionpoint group B that corresponds to the detection point group Brepresenting the object 30 and clustered based on the positioninformation of each detection point 32 at the first timing t1′,represents the same object 30, and is clustered based on the positioninformation of each detection point 32 at the second timing t2′. Thespecification function 14N specifies, as the target point T of theobject 30 at the first timing t1′, a position in the shape model Mfitted into the detection point group B at the first timing t1′,corresponding to the target point T′ set based on the shape model Mfitted into the detection point group B at the second timing t2′.

In this manner, the information processing device 14 according to thepresent embodiment specifies the target point T of the object 30included in the observation information at a timing to be processed byusing the target point T′ specified based on the observation informationat a different timing.

Thus, the information processing device 14 according to the presentembodiment can reliably and accurately specify the target point T inaddition to the effect of the first embodiment. The informationprocessing device 14 according to the present embodiment can achieve atleast one of reduction of a processing load on the processing circuit14A and improvement of a processing speed in addition to the effect ofthe first embodiment.

Third Embodiment

FIG. 11 is a block diagram of an exemplary configuration of aninformation processing device 16 according to the present embodiment.The information processing device 16 according to the present embodimentfits a plurality of the shape models M to the detection point group Band specifies the target point T of the object 30 based on the shapemodels M.

Similarly to the information processing devices 10 and 14 according tothe above-described embodiments, the information processing device 16specifies the target point T of the object 30. The informationprocessing device 16 is, for example, a dedicated or generalizedcomputer.

The information processing device 16 includes a processing circuit 16A,the storage circuit 10B, the output circuit 10C, the sensor 10G, and theinput device 10H. The information processing device 16 is same as theinformation processing device 10 according to the first embodimentexcept for including the processing circuit 16A in place of theprocessing circuit 10A.

The processing circuit 16A includes the acquisition function 10K, thecategorization function 10L, a shape fitting function 16M, aspecification function 16N, and the output control function 10P. Theprocessing circuit 16A is same as the processing circuit 10A except forincluding the shape fitting function 16M and the specification function16N in place of the shape fitting function 10M and the specificationfunction 10N.

The shape fitting function 16M fits a plurality of the shape models Mdifferent from each other in at least one of shape and position to thedetection point group B clustered by the categorization function 10L. Inother words, in the present embodiment, the shape fitting function 16Mfits a plurality of the shape models M to the one detection point groupB.

Similarly to the shape fitting function 10M according to the firstembodiment, the shape fitting function 16M fits each shape model M. Inthe first embodiment, for each detection point group B representing oneobject 30, the shape fitting function 10M slightly changes variables ofan initial shape model and fits one initial shape model that minimizesthe evaluating function as the shape model M to the detection pointgroup B projected onto the horizontal plane. In the present embodiment,however, for each detection point group B representing one object 30,the shape fitting function 10M applies, as a plurality of the shapemodels M, a plurality of initial shape models predetermined in ascendingorder of the evaluating function.

FIG. 12 illustrates explanatory diagrams of specification of the targetpoint T by the processing circuit 16A. In FIG. 12, (A) to (C) illustratea case in which two shape models M (a shape model M10 (refer to (A) inFIG. 12) and a shape model M11 (refer to (B) in FIG. 12)) having shapesdifferent from each other are fitted into the detection point group Bconsisting of detection points 32. The phenomenon illustrated in (A) to(C) of FIG. 12 occurs due to change of an observed surface on the object30 in some cases.

The specification function 16N specifies the target point T of theobject 30 represented by the detection point group B based on the shapemodels M fitted into the detection point group B.

FIG. 12C illustrates a state in which the shape models M (shape modelsM10 and M11) illustrated in (A) and (B) of FIG. 12 are placed over suchthat the positions of the detection points 32 match with each other.

The specification function 16N specifies, as the target point T, one ofcandidate points C set to each of the applied shape models M (candidatepoints C1′ to C4′ of the shape model M11, and candidate points C1 to C4of the shape model M10). The method employed by the specificationfunction 10N in the first embodiment may be employed as a method ofspecifying one candidate point C as the target point T from among thecandidate points C.

For example, the specification function 16N may specify, as the targetpoint T, the candidate point C closest to the position of the sensor 10G(for example, the candidate point C4) among the candidate points C(candidate points C1′ to C4′ of the shape model M11 and candidate pointsC1 to C4 of the shape model M10) set to each of the shape models M.

The specification function 16N may specify the target point T by usingeach average position of candidate points C existing in the same regionfor the shape models M fitted into the detection point group B. The sameregion is a region at the same position between the shape models M.

FIG. 13 illustrates explanatory diagrams of specification of the targetpoint T by the processing circuit 16A. In FIG. 13, (A) to (G) illustratea case in which five shape models M (shape models M20 to M24 (refer to(A) to (E) in FIG. 13)) at positions different from each other arefitted into the detection point group B consisting of detection points32. The phenomenon illustrated in FIG. 13 occurs when a plurality of theshape models M for which the value of the evaluating function is small(the value of the evaluating function is smaller than a predeterminedvalue) exist near the shape of an actual object.

In FIG. 13, (F) illustrates a state in which detection points 32 of eachof the shape models M (shape models M20 to M24) illustrated in (A) to(E) of FIG. 13 are fitted into a rectangle representing the actual shapeof the object 30.

The specification function 16N calculates an average position ofcandidate points C set to the shape models M (shape models M20 to M24)for each of groups (groups C10 to C13) of candidate points C in the sameregion between the shape models M. Then, the calculated averagepositions are used as candidate points C (C10′, C11′, C12′, and C13′)corresponding to the detection point group B.

Then, the specification function 16N specifies one of these candidatepoints C (C10′, C11′, C12′, and C13′) as the target point T. The methodemployed by the specification function 10N in the first embodiment maybe employed as a method of specifying one candidate point C as thetarget point T from among the candidate points C (C10′, C11′, C12′, andC13′). The specification function 16N may use a middle point or weightedaverage of candidate points C in place of the average position ofcandidate points C.

The following describes a procedure of the processing executed by theprocessing circuit 16A. FIG. 14 is a flowchart of an exemplary procedureof the processing executed by the processing circuit 16A.

First, the acquisition function 10K acquires the observation informationfrom the sensor 10G (step S300). Subsequently, the acquisition function10K acquires the position information of each detection point 32 fromthe observation information acquired at step S300 (step S302).

Subsequently, the categorization function 10L clusters detection points32, the position information of which is acquired at step S302, into thedetection point group B representing the object 30 (step S304).Subsequently, the shape fitting function 16M fits a plurality of theshape models M into the detection point group B clustered at step S304(step S306).

Subsequently, the specification function 16N specifies the target pointT based on the shape models M applied at step S306 (step S308).Subsequently, the specification function 16N outputs the outputinformation indicating the target point T specified at step S308 (stepS310).

Subsequently, the processing circuit 16A determines whether to end theprocessing (step S312). For example, the processing circuit 16A performsthe determination at step S312 by determining whether a signalindicating end of the processing has been received from the input device10H. If the determination at step S312 is negative (No at step S312),the present routine returns to the above-described step S300. If thedetermination at step S312 is positive (Yes at step S312), the presentroutine is ended.

As described above, in the information processing device 16 according tothe present embodiment, the shape fitting function 16M fits, into thedetection point group B, a plurality of the shape models M differentfrom each other in at least one of shape and position. The specificationfunction 16N specifies the target point T of the object 30 based on theshape models M.

Accordingly, the information processing device 16 according to thepresent embodiment can reliably specify the target point T of the object30 when the application of the shape model M is unreliable.

Accordingly, the information processing device 16 according to thepresent embodiment can more accurately specify the target point T of theobject 30 in addition to the effect of the information processing device10 according to the first embodiment described above.

The following describes an exemplary hardware configuration of theinformation processing devices 10, 14, and 16 according to theabove-described embodiments. FIG. 15 is an exemplary hardwareconfiguration diagram of the information processing devices 10, 14, and16 according to the above-described embodiments.

The information processing devices 10, 14, and 16 according to theabove-described embodiments each include a control device such as acentral processing unit (CPU) 86, a storage device such as a read onlymemory (ROM) 88, a random access memory (RAM) 90, or a hard disk drive(HDD) 92, an I/F unit 82 as an interface to various instruments, anoutput unit 80 configured to output various kinds of information such asthe output information, an input unit 94 configured to receive anoperation by the user, and a bus 96 connecting these components, andeach have a hardware configuration using a normal computer.

In the information processing devices 10, 14, and 16 according to theabove-described embodiments, each above-described function is achievedon the computer by the CPU 86 loading a computer program from the ROM 88onto the RAM 90 and executing the computer program.

A computer program for executing the above-described processing executedby the information processing devices 10, 14, and 16 according to theabove-described embodiments is stored in the HDD 92. Alternatively, thecomputer program for executing the above-described processing executedby the information processing devices 10, 14, and 16 according to theabove-described embodiments may be incorporated in the ROM 88 in advanceand provided.

The computer program for executing the above-described processingexecuted by the information processing devices 10, 14, and 16 accordingto the above-described embodiments may be stored as a file in aninstallable format or an executable format in a computer-readablestorage medium such as a CD-ROM, a CD-R, a memory card, a digitalversatile disc (DVD), or a flexible disk (FD) and provided as a computerprogram product. Alternatively, the computer program for executing theabove-described processing executed by the information processingdevices 10, 14, and 16 according to the above-described embodiments maybe stored on a computer connected to a network such as the Internet andmay be provided by downloading through the network. Alternatively, thecomputer program for executing the above-described processing executedby the information processing devices 10, 14, and 16 according to theabove-described embodiments may be provided or distributed through anetwork such as the Internet.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An information processing device comprising: amemory; and processing circuitry configured to operate as: an acquirerconfigured to acquire position information of detection points; acategorizer configured to cluster the detection points into one or moredetection point groups each representing an object; a shape fitterconfigured to fit a predetermined shape model into each detection pointgroup on the basis of the position information of the clustereddetection points belonging to the each detection point group; and aspecifier configured to specify a target point that is a single pointrepresenting the object on the basis of the shape model, wherein theprocessing circuitry is further configured to operate as a searcherconfigured to search for detection point groups clustered on the basisof the position information at different timings of at least a firsttiming and a second timing, a detection point group at the first timingand a detection point group at the second timing representing anidentical object, and the specifier specifies, as the target point ofthe object at the first timing, a position in the shape model fittedinto the detection point group at the first timing, the positioncorresponding to the target point set on the basis of the shape modelfitted into the detection point group at the second timing.
 2. Thedevice according to claim 1, wherein the acquirer acquires the positioninformation of the detection points from observation information onsurrounding of a sensor.
 3. The device according to claim 1, wherein thespecifier specifies, as the target point, one of candidate points set tothe shape model in advance.
 4. The device according to claim 2, whereinthe specifier specifies, as the target point, a candidate point closestto the sensor among the candidate points set to the shape model inadvance.
 5. The device according to claim 1, wherein the specifierspecifies, as the target point, a candidate point having a highestdistribution rate of the detection points among candidate points set tothe shape model in advance.
 6. The device according to claim 1, whereinthe specifier specifies, as the target point, a candidate point closestto any one of the detection points among candidate points set to theshape model in advance.
 7. The device according to claim 1, wherein theshape fitter fits, into the detection point group, shape models that aredifferent from each other in at least one of a shape and a position, andthe specifier specifies the target point of the object on the basis ofthe shape models.
 8. The device according to claim 2, wherein the sensorincludes at least one of a distance sensor and an image capturingdevice.
 9. The device according to claim 1 mounted on a moving object ora stationary object.
 10. The device according to claim 1, wherein theprocessing circuitry is further configured to operate as an output unitconfigured to output output information including information indicatingthe specified target point.
 11. An information processing methodcomprising: acquiring position information of detection points;clustering the detection points into one or more detection point groupseach representing an object; fitting a predetermined shape model intoeach detection point group on the basis of the position information ofthe clustered detection points belonging to the each detection pointgroup; and specifying a target point that is a single point representingthe object on the basis of the shape model, wherein detection pointgroups clustered on the basis of the position information at differenttimings of at least a first timing and a second timing are searched, anda detection point group at the first timing and a detection point groupat the second timing represent an identical object, and as the targetpoint of the object at the first timing, a position in the shape modelfitted into the detection point group at the first timing is specified,the position corresponding to the target point set on the basis of theshape model fitted into the detection point group at the second timing.12. The method according to claim 11, wherein the position informationof the detection points is acquired from observation information onsurrounding of a sensor.
 13. The method according to claim 11, whereinone of candidate points set to the shape model in advance is specifiedas the target point.
 14. The method according to claim 12, wherein acandidate point closest to the sensor is specified as the target pointamong candidate points set to the shape model in advance.
 15. The methodaccording to claim 11, wherein a candidate point having a highestdistribution rate of the detection points is specified as the targetpoint among candidate points set to the shape model in advance.
 16. Themethod according to claim 11, wherein a candidate point closest to anyone of the detection points is specified as the target point amongcandidate points set to the shape model in advance.
 17. The methodaccording to claim 11, wherein shape models that are different from eachother in at least one of a shape and a position are fit into thedetection point group, and the target point of the object is specifiedon the basis of the shape models.
 18. The method according to claim 12,wherein the sensor includes at least one of a distance sensor and animage capturing device.